null hypothesis: there is no relation between gender favorability of poor people
my hypothesis: females favor poor people more than males
ttest Welch Two Sample t-test data: nes$poorppl by nes$gender t = -4.8338, df = 1898.308, p-value = 1.447e-06 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -5.950301 -2.515468 sample estimates:
mean in group 1. Male respondent selected 72.14640
mean in group 2. Female respondent selected 76.37928
p-value is 1.447e-06, which is less than .05, so reject the null hypothesis. there *is* a relation between the two variables, it's not likely to get so close to zero, that there is no relation between the two variables.
females are slightly more likely to favor poor people than males do.
class(x$attrally) # find what kind of variable this is
summary(x$attrally) # summarizes variable
x$attrally<-factor(x$attrally, levels=c("have not done it and would never do it", "have not done it but might do it", "have done it in the more distant past","have done it in the past yr")) # Re-orders, strings together the categories
install.packages("WDI") #installs World Development Indicators package
library(WDI) #loads the package
WDIsearch("arms") #search for variables involving arms. search any term to find variables involving it.
?WDI #find webpage with code
WDI(country=c("IL","ZW","BT"), indicator=c("SH.XPD.PUBL", "SH.MED.CMHW.P3"), start=1960, end=2011, extra=FALSE) #compare countries based on a particular variable or variables
x$blahblahblah<-x$variablecode #name a variable in the "x" dataframe "blahblahblah"
HOMELESSNESS IN THE UNITED STATES, MENTAL HEALTH, ILLICIT DRUG USE, AND CRIME RATES
Ideas:
- Access to mental healthcare contingent on homeless being arrested for crimes
- Homelessness as a result of deinstitutionalization
- Substance abuse/drug arrests are huge part of criminal justice system
- Substance abuse/drug arrests are huge part of criminal justice system
- Facilities for substance abuse treatment
CODE AND ANALYSIS
File contains example code from class with a few annotations.
Codebook for General Social Survey Data
Or if you wish to save the codebook to your computer...
null hypothesis: there is no relation between gender favorability of poor people
my hypothesis: females favor poor people more than males
class(x$attrally) # find what kind of variable this is
summary(x$attrally) # summarizes variable
x$attrally<-factor(x$attrally, levels=c("have not done it and would never do it", "have not done it but might do it", "have done it in the more distant past","have done it in the past yr")) # Re-orders, strings together the categories
x$attrally<-factor(x$attrally, labels=c("1", "2", "3","4"))
x$rallynumeric<-as.numeric(x$attrally) # make your own variable, name it "rallynumeric" because you're changing the factor to a numeric variable
summary(x$rallynumeric)
tab<-table(x$attrally, x$race)
chisq.test(tab)
mosaicplot(tab)
?mosaicplot
install.packages("WDI") #installs World Development Indicators package
library(WDI) #loads the package
WDIsearch("arms") #search for variables involving arms. search any term to find variables involving it.
?WDI #find webpage with code
WDI(country=c("IL","ZW","BT"), indicator=c("SH.XPD.PUBL", "SH.MED.CMHW.P3"), start=1960, end=2011, extra=FALSE) #compare countries based on a particular variable or variables
x$blahblahblah<-x$variablecode #name a variable in the "x" dataframe "blahblahblah"
WDIsearch("health")
MORE INFORMATION AND ARTICLES
Guidelines with R and Research Paper
http://ramnathv.github.com/slidify/
Homeless Information
Arrest Data at US Census Bureau
Substance Abuse Treatment Facilities at US Census Bureau
Healthcare Expenditures at US Census Bureau
Mental Health Data
GROUP MEMBERS:
Name
e-Mail Address
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